/*
 *********************************************************************
 *                                                                   *
 *                           Open Bloom Filter                       *
 *                                                                   *
 * Author: Arash Partow - 2000                                       *
 * URL: http://www.partow.net                                        *
 * URL: http://www.partow.net/programming/hashfunctions/index.html   *
 *                                                                   *
 * Copyright notice:                                                 *
 * Free use of the Open Bloom Filter Library is permitted under the  *
 * guidelines and in accordance with the most current version of the *
 * Common Public License.                                            *
 * http://www.opensource.org/licenses/cpl1.0.php                     *
 *                                                                   *
 *********************************************************************
 */


#ifndef INCLUDE_BLOOM_FILTER_HPP
#define INCLUDE_BLOOM_FILTER_HPP

#include <cstddef>
#include <algorithm>
#include <cmath>
#include <limits>
#include <string>
#include <vector>

namespace Tidy
{

	static const std::size_t bits_per_char = 0x08;    // 8 bits in 1 char(unsigned)
	static const unsigned char bit_mask[bits_per_char] = {
		0x01,  //00000001
		0x02,  //00000010
		0x04,  //00000100
		0x08,  //00001000
		0x10,  //00010000
		0x20,  //00100000
		0x40,  //01000000
		0x80   //10000000
	};

	class bloom_parameters
	{
		public:

			bloom_parameters()
				: minimum_size(1),
				maximum_size(std::numeric_limits<unsigned long long int>::max()),
				minimum_number_of_hashes(1),
				maximum_number_of_hashes(std::numeric_limits<unsigned int>::max()),
				projected_element_count(10000),
				false_positive_probability(1.0 / projected_element_count),
				random_seed(0xA5A5A5A55A5A5A5AULL)
		{}

			virtual ~bloom_parameters()
			{}

			inline bool operator!()
			{
				return (minimum_size > maximum_size)      ||
					(minimum_number_of_hashes > maximum_number_of_hashes) ||
					(minimum_number_of_hashes < 1)     ||
					(0 == maximum_number_of_hashes)    ||
					(0 == projected_element_count)     ||
					(false_positive_probability < 0.0) ||
					(std::numeric_limits<double>::infinity() == std::abs(false_positive_probability)) ||
					(0 == random_seed)                 ||
					(0xFFFFFFFFFFFFFFFFULL == random_seed);
			}

			//Allowed min/max size of the bloom filter in bits
			unsigned long long int minimum_size;
			unsigned long long int maximum_size;

			//Allowed min/max number of hash functions
			unsigned int minimum_number_of_hashes;
			unsigned int maximum_number_of_hashes;

			//The approximate number of elements to be inserted
			//into the bloom filter, should be within one order
			//of magnitude. The default is 10000.
			unsigned long long int projected_element_count;

			//The approximate false positive probability expected
			//from the bloom filter. The default is the reciprocal
			//of the projected_element_count.
			double false_positive_probability;

			unsigned long long int random_seed;

			struct optimal_parameters_t
			{
				optimal_parameters_t()
					: number_of_hashes(0),
					table_size(0)
				{}

				unsigned int number_of_hashes;
				unsigned long long int table_size;
			};

			optimal_parameters_t optimal_parameters;

			virtual bool compute_optimal_parameters()
			{
				/*
Note:
The following will attempt to find the number of hash functions
and minimum amount of storage bits required to construct a bloom
filter consistent with the user defined false positive probability
and estimated element insertion count.
*/

				if (!(*this))
					return false;

				double min_m = std::numeric_limits<double>::infinity();
				double min_k = 0.0;
				double curr_m = 0.0;
				double k = 1.0;

				while (k < 1000.0)
				{
					double numerator   = (- k * projected_element_count);
					double denominator = std::log(1.0 - std::pow(false_positive_probability, 1.0 / k));
					curr_m = numerator / denominator;
					if (curr_m < min_m)
					{
						min_m = curr_m;
						min_k = k;
					}
					k += 1.0;
				}

				optimal_parameters_t& optp = optimal_parameters;

				optp.number_of_hashes = static_cast<unsigned int>(min_k);
				optp.table_size = static_cast<unsigned long long int>(min_m);
				optp.table_size += (((optp.table_size % bits_per_char) != 0) ? (bits_per_char - (optp.table_size % bits_per_char)) : 0);

				if (optp.number_of_hashes < minimum_number_of_hashes)
					optp.number_of_hashes = minimum_number_of_hashes;
				else if (optp.number_of_hashes > maximum_number_of_hashes)
					optp.number_of_hashes = maximum_number_of_hashes;

				if (optp.table_size < minimum_size)
					optp.table_size = minimum_size;
				else if (optp.table_size > maximum_size)
					optp.table_size = maximum_size;

				return true;
			}

	};

	class bloom_filter
	{
		protected:

			typedef unsigned int bloom_type;
			typedef unsigned char cell_type;

		public:

			bloom_filter()
				: bit_table_(0),
				salt_count_(0),
				table_size_(0),
				raw_table_size_(0),
				projected_element_count_(0),
				inserted_element_count_(0),
				random_seed_(0),
				desired_false_positive_probability_(0.0)
		{}

			bloom_filter(const bloom_parameters& p)
				: bit_table_(0),
				projected_element_count_(p.projected_element_count),
				inserted_element_count_(0),
				random_seed_((p.random_seed * 0xA5A5A5A5) + 1),
				desired_false_positive_probability_(p.false_positive_probability)
		{
			salt_count_ = p.optimal_parameters.number_of_hashes;
			table_size_ = p.optimal_parameters.table_size;
			generate_unique_salt();
			raw_table_size_ = table_size_ / bits_per_char;
			bit_table_ = new cell_type[static_cast<std::size_t>(raw_table_size_)];
			std::fill_n(bit_table_,raw_table_size_,0x00);
		}

			bloom_filter(const bloom_filter& filter)
			{
				this->operator=(filter);
			}

			inline bool operator == (const bloom_filter& f) const
			{
				if (this != &f)
				{
					return
						(salt_count_                         == f.salt_count_)                         &&
						(table_size_                         == f.table_size_)                         &&
						(raw_table_size_                     == f.raw_table_size_)                     &&
						(projected_element_count_   == f.projected_element_count_)   &&
						(inserted_element_count_             == f.inserted_element_count_)             &&
						(random_seed_                        == f.random_seed_)                        &&
						(desired_false_positive_probability_ == f.desired_false_positive_probability_) &&
						(salt_                               == f.salt_)                               &&
						std::equal(f.bit_table_,f.bit_table_ + raw_table_size_,bit_table_);
				}
				else
					return true;
			}

			inline bool operator != (const bloom_filter& f) const
			{
				return !operator==(f);
			}

			inline bloom_filter& operator = (const bloom_filter& f)
			{
				if (this != &f)
				{
					salt_count_ = f.salt_count_;
					table_size_ = f.table_size_;
					raw_table_size_ = f.raw_table_size_;
					projected_element_count_ = f.projected_element_count_;
					inserted_element_count_ = f.inserted_element_count_;
					random_seed_ = f.random_seed_;
					desired_false_positive_probability_ = f.desired_false_positive_probability_;
					delete[] bit_table_;
					bit_table_ = new cell_type[static_cast<std::size_t>(raw_table_size_)];
					std::copy(f.bit_table_,f.bit_table_ + raw_table_size_,bit_table_);
					salt_ = f.salt_;
				}
				return *this;
			}

			virtual ~bloom_filter()
			{
				delete[] bit_table_;
			}

			inline bool operator!() const
			{
				return (0 == table_size_);
			}

			inline void clear()
			{
				std::fill_n(bit_table_,raw_table_size_,0x00);
				inserted_element_count_ = 0;
			}

			inline void insert(const unsigned char* key_begin, const std::size_t& length)
			{
				std::size_t bit_index = 0;
				std::size_t bit = 0;
				for (std::size_t i = 0; i < salt_.size(); ++i)
				{
					compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
					bit_table_[bit_index / bits_per_char] |= bit_mask[bit];
				}
				++inserted_element_count_;
			}

			template<typename T>
				inline void insert(const T& t)
				{
					// Note: T must be a C++ POD type.
					insert(reinterpret_cast<const unsigned char*>(&t),sizeof(T));
				}

			inline void insert(const std::string& key)
			{
				insert(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
			}

			inline void insert(const char* data, const std::size_t& length)
			{
				insert(reinterpret_cast<const unsigned char*>(data),length);
			}

			template<typename InputIterator>
				inline void insert(const InputIterator begin, const InputIterator end)
				{
					InputIterator itr = begin;
					while (end != itr)
					{
						insert(*(itr++));
					}
				}

			inline virtual bool contains(const unsigned char* key_begin, const std::size_t length) const
			{
				std::size_t bit_index = 0;
				std::size_t bit = 0;
				for (std::size_t i = 0; i < salt_.size(); ++i)
				{
					compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
					if ((bit_table_[bit_index / bits_per_char] & bit_mask[bit]) != bit_mask[bit])
					{
						return false;
					}
				}
				return true;
			}

			template<typename T>
				inline bool contains(const T& t) const
				{
					return contains(reinterpret_cast<const unsigned char*>(&t),static_cast<std::size_t>(sizeof(T)));
				}

			inline bool contains(const std::string& key) const
			{
				return contains(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
			}

			inline bool contains(const char* data, const std::size_t& length) const
			{
				return contains(reinterpret_cast<const unsigned char*>(data),length);
			}

			template<typename InputIterator>
				inline InputIterator contains_all(const InputIterator begin, const InputIterator end) const
				{
					InputIterator itr = begin;
					while (end != itr)
					{
						if (!contains(*itr))
						{
							return itr;
						}
						++itr;
					}
					return end;
				}

			template<typename InputIterator>
				inline InputIterator contains_none(const InputIterator begin, const InputIterator end) const
				{
					InputIterator itr = begin;
					while (end != itr)
					{
						if (contains(*itr))
						{
							return itr;
						}
						++itr;
					}
					return end;
				}

			inline virtual unsigned long long int size() const
			{
				return table_size_;
			}

			inline std::size_t element_count() const
			{
				return inserted_element_count_;
			}

			inline double effective_fpp() const
			{
				/*
Note:
The effective false positive probability is calculated using the
designated table size and hash function count in conjunction with
the current number of inserted elements - not the user defined
predicated/expected number of inserted elements.
*/
				return std::pow(1.0 - std::exp(-1.0 * salt_.size() * inserted_element_count_ / size()), 1.0 * salt_.size());
			}

			inline bloom_filter& operator &= (const bloom_filter& f)
			{
				/* intersection */
				if (
						(salt_count_  == f.salt_count_) &&
						(table_size_  == f.table_size_) &&
						(random_seed_ == f.random_seed_)
				   )
				{
					for (std::size_t i = 0; i < raw_table_size_; ++i)
					{
						bit_table_[i] &= f.bit_table_[i];
					}
				}
				return *this;
			}

			inline bloom_filter& operator |= (const bloom_filter& f)
			{
				/* union */
				if (
						(salt_count_  == f.salt_count_) &&
						(table_size_  == f.table_size_) &&
						(random_seed_ == f.random_seed_)
				   )
				{
					for (std::size_t i = 0; i < raw_table_size_; ++i)
					{
						bit_table_[i] |= f.bit_table_[i];
					}
				}
				return *this;
			}

			inline bloom_filter& operator ^= (const bloom_filter& f)
			{
				/* difference */
				if (
						(salt_count_  == f.salt_count_) &&
						(table_size_  == f.table_size_) &&
						(random_seed_ == f.random_seed_)
				   )
				{
					for (std::size_t i = 0; i < raw_table_size_; ++i)
					{
						bit_table_[i] ^= f.bit_table_[i];
					}
				}
				return *this;
			}

			inline const cell_type* table() const
			{
				return bit_table_;
			}

			inline std::size_t hash_count()
			{
				return salt_.size();
			}

		protected:

			inline virtual void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const
			{
				bit_index = hash % table_size_;
				bit = bit_index % bits_per_char;
			}

			void generate_unique_salt()
			{
				/*
Note:
A distinct hash function need not be implementation-wise
distinct. In the current implementation "seeding" a common
hash function with different values seems to be adequate.
*/
				const unsigned int predef_salt_count = 128;
				static const bloom_type predef_salt[predef_salt_count] =
				{
					0xAAAAAAAA, 0x55555555, 0x33333333, 0xCCCCCCCC,
					0x66666666, 0x99999999, 0xB5B5B5B5, 0x4B4B4B4B,
					0xAA55AA55, 0x55335533, 0x33CC33CC, 0xCC66CC66,
					0x66996699, 0x99B599B5, 0xB54BB54B, 0x4BAA4BAA,
					0xAA33AA33, 0x55CC55CC, 0x33663366, 0xCC99CC99,
					0x66B566B5, 0x994B994B, 0xB5AAB5AA, 0xAAAAAA33,
					0x555555CC, 0x33333366, 0xCCCCCC99, 0x666666B5,
					0x9999994B, 0xB5B5B5AA, 0xFFFFFFFF, 0xFFFF0000,
					0xB823D5EB, 0xC1191CDF, 0xF623AEB3, 0xDB58499F,
					0xC8D42E70, 0xB173F616, 0xA91A5967, 0xDA427D63,
					0xB1E8A2EA, 0xF6C0D155, 0x4909FEA3, 0xA68CC6A7,
					0xC395E782, 0xA26057EB, 0x0CD5DA28, 0x467C5492,
					0xF15E6982, 0x61C6FAD3, 0x9615E352, 0x6E9E355A,
					0x689B563E, 0x0C9831A8, 0x6753C18B, 0xA622689B,
					0x8CA63C47, 0x42CC2884, 0x8E89919B, 0x6EDBD7D3,
					0x15B6796C, 0x1D6FDFE4, 0x63FF9092, 0xE7401432,
					0xEFFE9412, 0xAEAEDF79, 0x9F245A31, 0x83C136FC,
					0xC3DA4A8C, 0xA5112C8C, 0x5271F491, 0x9A948DAB,
					0xCEE59A8D, 0xB5F525AB, 0x59D13217, 0x24E7C331,
					0x697C2103, 0x84B0A460, 0x86156DA9, 0xAEF2AC68,
					0x23243DA5, 0x3F649643, 0x5FA495A8, 0x67710DF8,
					0x9A6C499E, 0xDCFB0227, 0x46A43433, 0x1832B07A,
					0xC46AFF3C, 0xB9C8FFF0, 0xC9500467, 0x34431BDF,
					0xB652432B, 0xE367F12B, 0x427F4C1B, 0x224C006E,
					0x2E7E5A89, 0x96F99AA5, 0x0BEB452A, 0x2FD87C39,
					0x74B2E1FB, 0x222EFD24, 0xF357F60C, 0x440FCB1E,
					0x8BBE030F, 0x6704DC29, 0x1144D12F, 0x948B1355,
					0x6D8FD7E9, 0x1C11A014, 0xADD1592F, 0xFB3C712E,
					0xFC77642F, 0xF9C4CE8C, 0x31312FB9, 0x08B0DD79,
					0x318FA6E7, 0xC040D23D, 0xC0589AA7, 0x0CA5C075,
					0xF874B172, 0x0CF914D5, 0x784D3280, 0x4E8CFEBC,
					0xC569F575, 0xCDB2A091, 0x2CC016B4, 0x5C5F4421
				};

				if (salt_count_ <= predef_salt_count)
				{
					std::copy(predef_salt,
							predef_salt + salt_count_,
							std::back_inserter(salt_));
					for (unsigned int i = 0; i < salt_.size(); ++i)
					{
						/*
Note:
This is done to integrate the user defined random seed,
so as to allow for the generation of unique bloom filter
instances.
*/
						salt_[i] = salt_[i] * salt_[(i + 3) % salt_.size()] + static_cast<bloom_type>(random_seed_);
					}
				}
				else
				{
					std::copy(predef_salt,predef_salt + predef_salt_count,std::back_inserter(salt_));
					srand(static_cast<unsigned int>(random_seed_));
					while (salt_.size() < salt_count_)
					{
						bloom_type current_salt = static_cast<bloom_type>(rand()) * static_cast<bloom_type>(rand());
						if (0 == current_salt) continue;
						if (salt_.end() == std::find(salt_.begin(), salt_.end(), current_salt))
						{
							salt_.push_back(current_salt);
						}
					}
				}
			}

			inline bloom_type hash_ap(const unsigned char* begin, std::size_t remaining_length, bloom_type hash) const
			{
				const unsigned char* itr = begin;
				unsigned int loop = 0;
				while (remaining_length >= 8)
				{
					const unsigned int& i1 = *(reinterpret_cast<const unsigned int*>(itr)); itr += sizeof(unsigned int);
					const unsigned int& i2 = *(reinterpret_cast<const unsigned int*>(itr)); itr += sizeof(unsigned int);
					hash ^= (hash <<  7) ^  i1 * (hash >> 3) ^
						(~((hash << 11) + (i2 ^ (hash >> 5))));
					remaining_length -= 8;
				}
				while (remaining_length >= 4)
				{
					const unsigned int& i = *(reinterpret_cast<const unsigned int*>(itr));
					if (loop & 0x01)
						hash ^=    (hash <<  7) ^  i * (hash >> 3);
					else
						hash ^= (~((hash << 11) + (i ^ (hash >> 5))));
					++loop;
					remaining_length -= 4;
					itr += sizeof(unsigned int);
				}
				while (remaining_length >= 2)
				{
					const unsigned short& i = *(reinterpret_cast<const unsigned short*>(itr));
					if (loop & 0x01)
						hash ^=    (hash <<  7) ^  i * (hash >> 3);
					else
						hash ^= (~((hash << 11) + (i ^ (hash >> 5))));
					++loop;
					remaining_length -= 2;
					itr += sizeof(unsigned short);
				}
				if (remaining_length)
				{
					hash += ((*itr) ^ (hash * 0xA5A5A5A5)) + loop;
				}
				return hash;
			}

			std::vector<bloom_type> salt_;
			unsigned char*          bit_table_;
			unsigned int            salt_count_;
			unsigned long long int  table_size_;
			unsigned long long int  raw_table_size_;
			unsigned long long int  projected_element_count_;
			unsigned int            inserted_element_count_;
			unsigned long long int  random_seed_;
			double                  desired_false_positive_probability_;
	};

	inline bloom_filter operator & (const bloom_filter& a, const bloom_filter& b)
	{
		bloom_filter result = a;
		result &= b;
		return result;
	}

	inline bloom_filter operator | (const bloom_filter& a, const bloom_filter& b)
	{
		bloom_filter result = a;
		result |= b;
		return result;
	}

	inline bloom_filter operator ^ (const bloom_filter& a, const bloom_filter& b)
	{
		bloom_filter result = a;
		result ^= b;
		return result;
	}

	class compressible_bloom_filter : public bloom_filter
	{
		public:

			compressible_bloom_filter(const bloom_parameters& p)
				: bloom_filter(p)
			{
				size_list.push_back(table_size_);
			}

			inline virtual unsigned long long int size() const
			{
				return size_list.back();
			}

			inline bool compress(const double& percentage)
			{
				if ((0.0 >= percentage) || (percentage >= 100.0))
				{
					return false;
				}

				unsigned long long int original_table_size = size_list.back();
				unsigned long long int new_table_size = static_cast<unsigned long long int>((size_list.back() * (1.0 - (percentage / 100.0))));
				new_table_size -= (((new_table_size % bits_per_char) != 0) ? (new_table_size % bits_per_char) : 0);

				if ((bits_per_char > new_table_size) || (new_table_size >= original_table_size))
				{
					return false;
				}

				desired_false_positive_probability_ = effective_fpp();
				cell_type* tmp = new cell_type[static_cast<std::size_t>(new_table_size / bits_per_char)];
				std::copy(bit_table_, bit_table_ + (new_table_size / bits_per_char), tmp);
				cell_type* itr = bit_table_ + (new_table_size / bits_per_char);
				cell_type* end = bit_table_ + (original_table_size / bits_per_char);
				cell_type* itr_tmp = tmp;

				while (end != itr)
				{
					*(itr_tmp++) |= (*itr++);
				}

				delete[] bit_table_;
				bit_table_ = tmp;
				size_list.push_back(new_table_size);

				return true;
			}

		private:

			inline virtual void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const
			{
				bit_index = hash;
				for (std::size_t i = 0; i < size_list.size(); ++i)
				{
					bit_index %= size_list[i];
				}
				bit = bit_index % bits_per_char;
			}

			std::vector<unsigned long long int> size_list;
	};
};

#endif


/*
  Note 1:
  If it can be guaranteed that bits_per_char will be of the form 2^n then
  the following optimization can be used:

  hash_table[bit_index >> n] |= bit_mask[bit_index & (bits_per_char - 1)];

  Note 2:
  For performance reasons where possible when allocating memory it should
  be aligned (aligned_alloc) according to the architecture being used.
*/
